Churn prediction and social neighbour influences for different types of user groups in virtual worlds

Liu, Duen‐Ren; Liao, Hsiu Yu; Chen, Kuan‐Yu; Chiu, Yi Ling

doi:10.1111/exsy.12384

Cited by 6 publications

(5 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yet, since large-scale data on customers and their social interactions are more available for current customers than for potential ones, we believe that the use of AI to conduct social network analysis will have higher impact on customer development and retention. This will be particularly relevant to products such as digital games, where customers' social network activities are routinely collected (Liu, Liao, Chen, & Chiu, 2019).…”

Section: Role Of Social Networkmentioning

confidence: 99%

Brave New World? On AI and the Management of Customer Relationships

Libai

Bart

Gensler

et al. 2020

Journal of Interactive Marketing

224

106

View full text Add to dashboard Cite

In light of the emerging discourse on AI systems' effect on society, whose perception swings widely between utopian and dystopian, we conduct herein a critical analysis of how artificial intelligence (AI) affects the essential nature of customer relationship management (CRM). To do so, we survey the AI capabilities that will transform CRM into AI-CRM and examine how the transformation will influence customer acquisition, development, and retention. We highlight in particular how AI-CRM's improving ability to predict customer lifetime value will generate an inexorable rise in implementing adapted treatment of customers, leading to greater customer prioritization and service discrimination in markets. We further consider the consequences for firms and the challenges to regulators.

show abstract

Section: Role Of Social Networkmentioning

confidence: 99%

Brave New World? On AI and the Management of Customer Relationships

Libai

Bart

Gensler

et al. 2020

Journal of Interactive Marketing

224

106

View full text Add to dashboard Cite

show abstract

“…The most commonly used features describe player behavior and performance, but in addition, social interaction features (Fu et al 2017), cognitive psychological features (Jeon et al 2017), and influence features (Fu et al 2017) have shown to improve the performance of the models. Unsupervised approaches have also been developed, including segmentation of players (Fu et al 2017), difference in social neighbour influence in different user groups (Liu et al 2019), and distinct behavioral profiles associated with churners and active players (Borbora and Srivastava 2012). The sole research we found that used social networks to predict churn, included information from ego-nets showing that loners are much more likely to churn than the socializers, and that the propensity to churn increases with decreased socialization (Borbora et al 2019).…”

Section: Churn Prediction In Online and Mobile Gamingmentioning

confidence: 99%

Social networks for enhanced player churn prediction in mobile free-to-play games

et al. 2022

View full text Add to dashboard Cite

Social networks have been shown to enhance player experience in online games and to be important for the players, who often build complex communities. In online and mobile games, the behavior of players is bursty as they tend to play intensively at first for a short time and then quit playing altogether. Such players are known as churners. In the literature, several attempts have been made at predicting player churn in online and mobile games using behavioral features from the games’ player logs as input in supervised machine learning models. Previous research shows that information from social networks provides alternative and significant information when predicting churn, and yet the importance of networks has not been fully researched in mobile gaming. In this research, we study player churn in a mobile free-to-play game with one-versus-one matches. We build two types of networks based on how two players are matched. We train churn prediction models with features extracted from the networks to evaluate their predictive performance in terms of churn. Furthermore, we predict churn using the players’ behavioral features during their first day of game playing. According to our results, the network features greatly increase the predictive performance of the models, indicating that they carry alternative information about intention to churn. In addition, the first-day features are quite predictive, which means that first day activity is sufficient to predict churn of players quite accurately, validating the bursty behavior. Our research gives an indication of which aspects of game playing are associated with churn and allow us to study influence and social factors in mobile games.

show abstract

“…Numerous works predict churn in various domains (Ahn et al 2020). Focusing on games, some utilize only the time spent playing (Milošević, Živić, and Andjelković 2017;Kummer, Nievola, and Paraiso 2018), others use social aspects (Liu et al 2019), some see the problem as time-series (Yang et al 2019), others utilize Natural Language Processing (Kilimci, Yörük, and Akyokus 2020). They use data from different game genres such as Multiplayer Online Battle Arena (MOBA) and Massively Multiplayer Online Role-Playing Game (MMORPG), from different platforms, like desktop and mobile, and target diverse types of players.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Profit by Mitigating Recurrent Churn Labeling Issues: Analysis from the Game Domain

Humenhuk

Kummer

Paraíso

2021

AIIDE

View full text Add to dashboard Cite

Churn can be interpreted as customer defection and can be considered one of the most critical challenges in the Game Analytics domain because of its impact on the game industry's profit. When predicting churn, the first step is defining what is considered churn, which can change depending on the players' behaviors and approaches. This work studied related works and revealed two recurrent issues in the labeling process: limitations on the adopted labeling approaches (1) and the static definition of churn (2). To mitigate the first issue, an individualized labeling approach was deployed. To address the second one, a novel evaluation method, based on the impact of a change in the churn definition, was proposed. This method allowed the proposition of two new labeling approaches, which were included in the analysis. By comparing the labeling approaches in two games using a profit perspective, it was identified that the new ones present statistically significant benefits compared to the traditional ones. Regarding the evaluation method, its usage can justify when the redefinition of churn and the classifier's retraining should happen to improve profit. The results are valuable for the game context, potentially extended to other contexts by delivering more reliable labels and more validated classification performance.

show abstract

Churn prediction and social neighbour influences for different types of user groups in virtual worlds

Cited by 6 publications

References 61 publications

Brave New World? On AI and the Management of Customer Relationships

Brave New World? On AI and the Management of Customer Relationships

Social networks for enhanced player churn prediction in mobile free-to-play games

Optimizing Profit by Mitigating Recurrent Churn Labeling Issues: Analysis from the Game Domain

Contact Info

Product

Resources

About